105
On drafting and revision in translation:
a corpus linguistics oriented analysis of
translation process data
Fabio Alves, Daniel Couto Vale
Federal University of Minas Gerais, Brazil
[email protected], [email protected]
This paper reports on a study which investigates prototypical characteristics of the drafting
and revision phases of the translation process, mapped onto the sequential unfolding of micro
translation units into macro translation units (MTUs). By using Litterae, an annotation and
search tool designed to mark, annotate and extract XML files of key-logged translation
process data, the paper analyses the performance of 12 professional translators and classifies
their output as MTUs grouped into three categories: MTUs containing micro units which are
processed solely during the drafting phase (P1 type), MTUs containing micro units which are
processed once in the drafting phase and finalized in the revision phase (P2 type), and MTUs
containing micro units which are processed during the drafting phase and taken up again
during the revision phase (P3 type). The analysis points to a hierarchical structure in which
P1 is more predominant than P2 which, in turn, is more frequent than P3.
Keywords: drafting and revision patterns in translation, micro and macro translation units,
semi-automatic analysis of translation process data, corpus linguistics oriented approach
1
Introduction
Corpus linguistics tools have been applied to research in translation studies to
analyse large amounts of translated texts aiming at identifying prototypical
translation patterns (Olohan & Baker 2000; Hansen-Schirra, Neumann, Steiner 2007,
among others). Although insightful, the results of these studies do not provide
explanation for those intermediate solutions which are deleted in the course of text
production and do not surface in the target texts. Drawing on a different approach,
translation process research has a long-standing tradition of trying to account for
these interim versions which occur in the different phases of the translation process
(Alves 2007). However, research on translation process data from the perspective of
corpus linguistics is still quite incipient. CORPRAT, the Corpus on Process for the
Analysis of Translations, developed by LETRA, the Laboratory for Experimentation
in Translation (Pagano, Magalhães, Alves 2004) is perhaps the first attempt to apply a
corpus linguistics oriented approach to the analysis of translation process data. Until
last year CORPRAT only stored and retrieved translation process data for research
purposes. Lately, with the advent of the LITTERAE search tool (Alves & Vale 2009),
it became possible not only to store and retrieve translation process data in
Translation: Corpora, Computation, Cognition. Special Issue on Parallel Corpora: Annotation, Exploitation, Evaluation.
Volume 1, Number 1. December 2011.
ISSN 2193-6986
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On drafting and revision in translation
CORPRAT but also to mark, annotate and extract translation process data using
corpus linguistics tools. Thus, it is now possible to query large amounts of translation
process data semi-automatically, to identify prototypical patterns of online text
production in translation, and to assess its unfolding in terms of sequential steps
which can provide insights into instances of cognitive planning and cognitive effort
in translation.
This paper1 looks at prototypical traits of drafting and revision patterns from a
process-oriented perspective. To do so, it analyses translations carried out by 12
professional translators – six translating from English into Brazilian Portuguese and
six translating from German into Brazilian Portuguese. The aim of the paper is to
examine the unfolding of micro translation units into macro translation units (Alves
& Vale 2009; Alves, Pagano, Neumann, Steiner & Hansen-Schirra 2010) and to
describe which patterns can be ascribed prototypically to particular phases of the
translation process. It also sheds light onto hierarchical patterns which can be seen as
indicative of prototypical characteristics observed in different stages of the
translation process.
2
Theoretical underpinnings
2.1
Development of CORPRAT
Pagano, Magalhães & Alves (2004) describe the rationale for the design of
CORPRAT, the Corpus on Process for the Analysis of Translations. The database has
been designed to store larger sets of data related to the process of on-line text
production in translation. Over the past few years, the amount of data stored in it has
been expanded significantly. CORPRAT aims at providing further insights into the
translation process, raising new hypotheses and presenting more robust evidence to
support or refute general claims about the translation process.
Building on research that favours a small corpora approach (Ghadessy & Gao
2001) to corpus linguistics, CORPRAT stores five complementary kinds of files
generated
through
key
logging,
screen
recording,
eye
tracking,
recordings/transcriptions of retrospective protocols and questionnaires, allowing
inquiries of translation process data from different perspectives. CORPRAT data also
allows target text (TT) production to be examined as finished end products or as
interim versions portraying intermediate stages of target text production such as the
ones produced during or at the end of the drafting phase as well as during and at the
end of the revision phase (Jakobsen 2002).
The language pairs available in CORPRAT comprise Brazilian Portuguese and
either English, German or Spanish. Data from experimental research stored in the
1
Research developed within the framework of the SEGTRAD Project (Cognitive Segmentation and
Translation Memory Systems: investigating the interface between translators’ performance and
translation technology) was funded by the Brazilian Research Council (CNPq) grant n° 301270/20058.
TC3, Vol. 1, No. 1
107
corpus reflect the performance of subjects who vary from novice to expert
translators, and also include subject-domain experts who are not translators (Pagano
& Silva 2008). The combined files in CORPRAT are used to account for particular
traits and features in translation processes, including research on the acquisition of
translation competence (Alves & Gonçalves 2007), the role of inferential processes in
translation (Alves & Gonçalves 2003, Alves 2007), the role of procedural and
declarative knowledge in translation contexts (Alves 2005a), descriptions of cognitive
profiles of novice and expert translators (Alves 2005b, Magalhães & Alves 2006), the
relevance of domain knowledge as observed in the performance of subject-domain
experts who are not translators (Pagano & Silva 2008), the impact of time pressure
(Liparini Campos 2005) and translation technology (Alves & Liparini Campos 2009)
on the translation process, and also studies on the nature of translation units (see
Alves & Vale 2009 for a comprehensive account of this type of research).
2.2
Micro and macro translation units
According to Alves & Vale’s (2009) review of the literature on translation units (TU)
from the perspective of translation process research, a TU begins with a reading
phase that is registered as a pause by Translog key-logging and evolves in a
continuous production phase until it is interrupted by a pause. This pause may be a
pause for planning or searching for a translation alternative, an assessment of the
previous production or the beginning of a new reading phase. As the translation
process unfolds, a previously translated segment may be taken up again for revision,
deletion or just for consultation without any changes in the text being made. These
recurrent movements will be analysed in two ranks, what results in two correlated
types of units, namely a micro and a macro TU.
A micro TU is defined as the flow of continuous TT production – which may
incorporate the continuous reading of source and TT segments – separated by pauses
during the translation process as registered by key-logging and/or eye-tracking
software. It can be correlated to a ST segment that attracts the translator’s focus of
attention at a given moment. A macro TU, in turn, is defined as a collection of micro
TUs that comprises all the interim text productions that correspond to the translator’s
focus on the same ST segment from the first tentative rendering to the final output
that appears in the TT (see Alves & Vale 2009: 261 for a graphic description of a
micro/macro TU). Thus, a macro TU incorporates all the text production segments
(revisions, deletions, substitutions, etc.) in the unfolding of the process, mapped onto
the initial focus of attention which triggered a given micro TU. These production
segments can be annotated together as a sequence of micro TUs, which then make up
a macro TU. Micro and macro TUs consist of text production segments. For the sake
of operationalizing the two types of units, micro TUs will consist of a text production
segment, including deletions, additions and other possible changes implemented online, located between two pauses of arbitrary length, always below the standard
threshold of five/six seconds.
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On drafting and revision in translation
Alves & Vale (2009) illustrate the operationalization of these two concepts. From
an initial focus of attention2 on a given ST segment, several movements may be
implemented by the translator at different times of the translation process. Each of
these movements constitutes a micro TU until a definite solution is found. The
collection of processing steps, from the first draft to the final translation of the text
segment is considered to be a macro TU, that is, a macro TU is constituted by micro
TUs which are revisions carried out both on-line during the drafting phase and later
on at the end-revision phase 3. As such, revisions carried out while the TT is being
drafted can be contrasted and cross-analysed with revisions implemented during a
separate phase, after a first version of the TT has been completed.
This two-rank structure of macro TUs comprising one or several micro TUs is
proposed to enable the annotation and querying of relevant translation process data.
In this paper, we assume that the analysis of micro and macro TUs, both in the
drafting and revision phases, can provide direct evidence for describing different
levels of translation performance and identifying segmentation patterns related to
translation expertise (see also Alves, Pagano, Neumann, Steiner & Hansen-Schirra
2010 for an analysis of micro and macro translation units).
Bearing in mind that the translation unit (TU) and segmentation patterns play a
pivotal role in translation process research, one of the major goals behind the
development of CORPRAT is to investigate the size and the scope of translation units
as defined by Alves (2000). However, until recently, this had to be carried out
manually on relatively small samples. The advent of the LITTERAE search tool,
described in the next section, opens up a new avenue for translation process research.
2.3
On the development of LITTERAE: mapping micro and macro translation
units
LITTERAE4 is an annotation and search system designed and implemented as a
research tool that is used for storing, annotating and querying corpora of translations
comprising both texts and process data. In addition to the corpora, the system
2
3
4
A macro TU is a series of translation movements spread throughout the translation process in which
the translator writes and edits TT segments that correspond to the same ST segment. This series of
movements start with a focus of attention on the ST segment, the initial focus of attention, and ends
with the translator writing the correspondent TT segment that appears as the final product of the
translation. The initial focus of attention of a macro TU should not be understood as the translator
ocular foci on the screen in the beginning of each micro TU. While there may be one or more ocular
foci on both ST and TT in each micro TU, the initial focus of attention of a macro TU is always on the
ST and it is what triggers the macro TU.
A micro TU of drafting usually occurs during the drafting phase. Only when the translator misses or
deliberately postpones the translation of a segment of the ST, there is a micro TU of drafting during
the revision phase. Meanwhile, a micro TU of revision may occur both during the drafting and
revision phases of the translation process.
LITTERAE (http://letra.letras.ufmg.br/litterae) is the direct product of the Laboratory for
Experimentation in Translation (LETRA) at Federal University of Minas Gerais (UFMG) in Brazil.
TC3, Vol. 1, No. 1
109
includes a collocation search tool and functions for annotating and querying the
corpora.
In designing the annotation system, we have been guided by the following
assumptions that offer challenges, opportunities and restrictions:
1. The system is a web program. It must have a central database and allow group
work both within premises and by remote access.
2. The system does not impose any specific set of theoretical categories and allows
the multiple use of different theoretical approaches in the annotation process.
3. The system does not impose any language-specific or theory-specific grammatical
structure for its mark-up units. It provides a set abstraction that can mark up
discontinuous units at any rank of grammatical and process hierarchy as well as
marking up overlapping units. It does not represent composition or constituency
and the researcher cannot represent a unit may as composed or constitued by
others.5
4. The system keeps raw corpora and annotations separate (stand-off annotation)
and thus allows the creation of multiple annotation entries for the same corpus
entry. Differently from other systems that replicate raw corpus data in annotation
files, annotation entries in LITTERAE replicate no data while a single copy of the
raw corpora is kept.6
5. The system is designed for both individual and group work. Administrators have
control over which parts of the corpora can be accessed by each user, but not over
which functions each user may use. If a user has access to a corpus, he or she may
do any action the system allows to this corpus.
6. The system is tested against the latest versions of Gecko and Webkit render
engines, which are bundled with Firefox, Chrome and Safari web browsers and
which can be added as a plugin to Internet Explorer web browser. These
programs/applications are available for the most popular operating systems
(Windows, MacOS, Linux, iOS, and Android) free of charge.
Annotating a corpus entry consists of two steps: the first is marking up the corpus
entry and the second is tagging its mark-up units with categories. It is possible for a
translation process researcher to segment the process by any pause size down to one
millisecond, and as the tagging system does not impose any specific set of categories,
the researcher can decide which categories to use according to his or her researchspecific needs.
The only data abstraction that can be tagged within the annotation system is a TU,
operationalized as a set of chunks of a keylog file. By definition, a micro TU ends in a
continuous span of writing activity interrupted by a pause of a certain length (Alves
5
6
The process annotation is not multi-layer – clauses being composed by groups and phrases – nor
multi-strata – grammatical units representing meaning. It is intended to be a multi-version
annotation in which different versions of the same segment of the text are grouped together.
LITTERAE stores data in SQL Tables, therefore its annotations are entries and not files. Data is not
stored in XML files.
110
On drafting and revision in translation
2000). As each writing activity adds a new chunk to the keylog file, by grouping the
related writing activities, we are able to mark and tag the macro TU, but this set
abstraction may also be used to annotate individual micro TUs and sets of micro TUs
related in other ways. The choice of what to annotate is left open to the researchers.
Both the annotation and the corpus entries – texts and process key-logging
(generated by Translog 2006 and saved as XML files) – are stored on the same SQL
database. They are stored in different relational tables, which results in a completely
stand-off annotation. Each corpus entry can be annotated as many times as necessary
and the annotations do not interfere with the raw corpus nor with one another. This
separation of raw corpora and annotation is achieved by creating multiple distinct
isolated mark-ups for each corpus entry (text or process) and by keeping mark-up
units in mark-ups instead of inserting the mark-up units into the corpus entries. Each
mark-up is identified and stored separately as an isolated entry in a mark-up base
apart from the raw corpus base.
The mark-up units are individually tagged with research-specific categories. The
tags are also stored in the database separate from the units. When creating charts,
tables and querying the corpus, the researchers have the option of choosing a set of
annotations to produce a joint output with all related annotations of the research.
Translation process data are stored as raw corpora and are then ready to be
annotated. When annotation begins, the researcher will be able to replay the key-log
file and interactively select a set of micro units that constitute each macro unit of the
translation process. The annotation of mark-up units is implemented in a module of
the system code-named Enrich. This is where process data can be enriched on a
special replay screen for marking up macro TUs. Log files can be re-played and
viewed within different time intervals, the smallest one being one-second long. The
log file is then segmented by pauses whose value is determined in the box at the top
of the screen. Finally, annotations of mark-up units will appear. The system will store
annotated process data as macro TUs. Stored information can then be queried using
the labels applied in the annotation process.
The final stage of the system allows the querying of larger sets of process data
using the labels applied during the annotation process. As shown in Section 4,
researchers will be able to view the annotated macro TUs, search for a specific one,
and present the relative and absolute frequency of occurrence of categories as both
bar charts and tables. A complete account of the structure and functioning of
LITTERAE is found in Alves & Vale (2009).
3
3.1
Methodology
Research design and data collection
The experimental design used in this paper builds on Alves & Liparini Campos
(2009) for data collection and is an extension of Alves & Vale (2009) in terms of
categories of analysis. Two correlated source texts, one in English and one German,
consisting of extracts of approximately 500 words, collected from a technical manual,
TC3, Vol. 1, No. 1
111
were used as textual input. They contained instructions for the use of a blood sugar
meter in English (T1) and in German (T2).
Translations were carried out with access to online documentation sources and no
time pressure was introduced. Subjects’ performance was recorded with
Translog2000 and data was later converted into XML files with the aid of
Translog2006. Onscreen data not captured by Translog were recorded with the
software Camtasia which registered the unfolding of the translation process. Direct
observation allowed that notes on translator’s behaviour and consultations during
the translation task were registered by the researcher in pre-elaborated observation
charts.
All procedures followed the methodological approach known as data
triangulation (Alves 2003), which attempts to map the translation process using data
collected from different vantage points (see also Jakobsen 1999 for a discussion of this
technique originally used in the social sciences). Sources for triangulating translation
process data were the recordings of target text production in real time, direct
observation charts registering notes on translator’s consultation and behaviour, and
retrospective protocols. For the purpose of the present paper, only Translog XML
files were analysed with the aid of the LITTERAE search tool.
3.2
Methodology for data analysis
Data generated in the experiment consisted of 12 target texts in Brazilian Portuguese.
Pauses which occurred during their production were classified as micro units on the
basis of a five second pause interval. Each of these micro units received a time stamp.
Whenever these micro units remain unchanged throughout the translation process,
they are considered to be a macro unit. And whenever one of these micro units is
taken up again by the translators, they are grouped together and, as such, also
considered to be a macro unit. In this paper, we only analysed macro units of the
latter kind using the annotation procedures provided by LITTERAE. As a
methodological decision, micro units were classified as instances of online revision
when the subsequent micro unit was processed again still in the drafting phase.
These were grouped together and identified as a macro unit by their corresponding
time stamps and their editing was represented by a pipe [ | ]. When the micro unit
was taken up again in the end-revision phase, it was identified with a corresponding
time stamp which was far apart in terms of temporal dislocation from the preceding
micro unit in the drafting phase. This type of editing in the revision phase was
represented by a tilde [ ~ ].
Example 1
ned | medidor de açúcar | medidor do nível de açúcar – [P1]
112
On drafting and revision in translation
Example 2
fora do corpo ~ de forma invasiva – [P2]
Example 3
Medidor de índice | Medidor de glicemis ~ Medidor de glicemia – [P3]
Example 1 presents two revision steps and three versions of a text segment. It was
captured during the drafting phase in four chunks of the translog file. Together they
make up a macro unit. Editing within a macro unit is represented by a pipe [ | ]. As
shown in Figure 1, there are four chunks of writing activity in this macro unit: at
62730ms of the translation process the translator typed “(ned”; after approximately
two seconds, at 88830ms, the three first letters were deleted and “medidor de” [meter
of] was typed in; around two seconds later, at 106300ms, after a pause for internal
support, “açúcar no sangue)” [sugar in the blood] was typed; then, at 1228840ms, still
in the drafting phase, “do nível” [of the level] was inserted. This generated the end
product “medidor do nível de açúcar no sangue” [meter of the level of sugar in the
blood] or [blood sugar level meter] which appears in the TT7. This type of macro unit
was classified as P1, namely a macro unit with processing patterns which occur only
in the drafting phase.
Figure 1: Example of a macro translation unit type P1
In Example 2, two micro units were processed in different phases of the
translation process to make up a macro translation unit. As shown in Figure 2, first a
micro unit was observed in the drafting phase at 792480ms in a long text segment of
115 characters in which the expression “fora do corpo” [outside the body] appeared.
This provisional solution was only revised in the revision phase. After a first draft of
the target text had been produced, at 3596240ms the micro unit was changed into “de
forma invasiva” [in an invasive manner] which together with the first rendering makes
up a macro unit. Editing within a macro unit which occurs in the revision phase is
represented by a tilde [ ~ ]. This type of macro unit was classified as P2, namely a
macro unit with processing patterns which occur only once in the drafting phase and
are then taken up again during the revision phase.
7
The segment of the text that is targeted by micro TUs of edition is generally smaller than the entire
segment of text produced in micro TUs of revision. When representing the revision chain and the
iterim versions, we only present the smaller segments that are actually reviewed.
TC3, Vol. 1, No. 1
113
Figure 2: Example of a macro translation unit type P2
In Example 3, two micro units occur in the drafting phase as in a P1 type of macro
translation unit. However, differently from a P1 macro unit, there is also one (or
more) micro unit observed in the revision phase. As shown in Figure 3, at 58130ms
the micro unit was processed as “medidor de índice” [meter of index]. Next, still in the
drafting phase, it was changed into “medidor de glicemis” [meter of blood-sugar-leves
/typo/]. Then, at 2108600, during the revision phase, the typo “s” was deleted and
replaced by “a” to render “medidor de glicemia” [meter of blood-sugar-level]. This type
of macro unit was classified as P3, namely a macro unit with processing patterns
which occur more than once in the drafting phase and are taken up again once or
more in the revision phase.
Figure 3: Example of a macro translation unit type P3
In order to carry out the analysis of drafting and revision patterns, XML files with
translation process data from the 12 professional translators were segmented into
micro units. Each file was then annotaded manually on the basis of the triadic
classification, and micro units were classified as P1, P2 and P3. The same procedure
was applied to all 12 XML files with translation process data generated by
Translog2006.
For the sake of clarification,
we provide a link
http://letra.letras.ufmg.br/resources/2010_alves_vale.png with access to three
appendixes where data analysis is fully displayed. Appendix 1 contains a set of
annotated macro units of type P1 whereas Appendix 2 comprises all macro units
classified as P2 and Appendix 3 shows the remaining macro units classified as P3.
Using these three categories, all micro units registered in the 12 keylog files with
translation process data were annotated as macro units. The next section presents the
results of this classification.
114
4
On drafting and revision in translation
Data analysis
In accordance with the proposal made by Alves & Vale (2009) to classify micro and
macro translation units, our corpus contains 355 macro units implemented by the 12
subjects. Table 1 shows the total number of macro units, made up by a combination
of P1, P2 and P3 types.
By looking at this table, one can easily identify a completely different pattern in
E5 with 58 occurrences of type P2 and only 4 cases of P1 and 1 case of P3. The next
highest count in this category is observed in the performance of G1 with 29
occurences of P2. If we consider E5 as an outlier, the total number of P1 will be 147,
with 129 cases of P2 and 16 occurrences of P3, indicating that, on the whole, P1 > P2 >
P3. As we have different profiles and different revision total frequencies, the total
numbers of P1, P2, and P3 are not informative in themselves. Comparing total P1 and
total P2 will result in different rules depending on the profiles we exclude. However,
regardless of considering E5 as an outlier or not, P1 and P2 occurrences are far higher
than P3 types which makes only 4.8% of the total number of occurrences in the
sample.
Subject
Number of macro units
(P1 + P2 + P3) =
E1
(17 + 21 + 1) =
39
E2
(7 + 0 + 0) =
07
E3
(9 + 12 + 0) =
21
E4
(29 + 22 + 5) =
56
E5
(4 + 58 + 1) =
63
E6
(11 + 10 + 0) =
21
G1
(12 + 29 + 5) =
46
G2
(6 + 5 + 2) =
13
G3
(23 + 0 + 0) =
23
G4
(22 + 12 + 2) =
36
G5
(1 + 8 + 0) =
09
G6
(10 + 10 + 1) =
21
(151 + 187 + 17) =
355
Total
Table 1: Total number of macro units per subject
4.1
Identifying patterns of translation units and profiles during drafting and
revision
Table 2 presents the absolute and relative numbers across the sample, separating
data among the subjects who translated from English (E1-E6) and from German into
Brazilian Portuguese (G1-G6), grouping them according to P1, P2 and P3 types of
macro translation units and adding a column with a classification of translator
profiles which will be discussed further in this section.
TC3, Vol. 1, No. 1
Subject
115
P1
P2
P3
Profile
Sub-profile
NonRecursive
Abs.
Rel.
Abs.
Rel.
Abs.
Rel.
E1
17
43.7%
21
53.8%
1
2.6%
Drafter/Reviser
E2
7
100%
0
----
0
----
Drafter
E3
9
42.9%
12
57.1%
0
----
Drafter/Reviser
E4
E5
29
4
51.8%
6.3%
22
58
39.2%
92.0%
5
1
8.9%
1.6%
Drafter/Reviser
Reviser
E6
11
52.4%
10
47.6%
0
----
Drafter/Reviser
G1
G2
G3
G4
G5
12
6
23
22
1
26.1%
46.2%
100%
61.1%
11.1%
29
5
0
12
8
63.0%
38.5%
---33.3%
88.9%
5
2
0
2
0
10.9%
15.4%
---5.6%
----
Drafter/Reviser
Drafter/Reviser
Drafter
Drafter/Reviser
Reviser
G6
10
47.6%
10
47.6%
1
4.8%
Drafter/Reviser
NonRecursive
Recursive
NonRecursive
Recursive
Recursive
Recursive
NonRecursive
Table 2: Absolute and relative numbers for P1, P2 and P3 per subject and corresponding profiles
If we look at the apparently disparate figures displayed in Table 2, a picture of
idiosyncratic patterns might seem to be the first obvious conclusion. However, by
closer scrutiny we can identify correlated patterns across the two language pairs. On
the one hand, both E2 and G3 only show cases of P1 macro units whereas E5 and G5
display predominant occurrences of P2 macro units. On the other hand, the
remaining subjects show a pattern where P1 and P2 types of macro units compete in
terms of predominance and sometimes P1 > P2 and at other times P2 > P1. If we
apply a formula to the number of occurrences, we can classify the data into four
different translator profiles.
A translator was classified with the profile of a “Drafter” if, during the drafting
phase, he or she revised the TT six times more than during the revision phase.
Inversely, a translator was classified with the profile of a “Reviser” if, during the
revision phase, he or she revised the TT six times more than during the drafting
phase. The remaining translators were classified with the profile of a
“Drafter/Reviser”. Within this group, we found two special subgroups comprised by
translators who either revised the same parts of the TT both during the drafting and
the revision phases, revisions of the type P3 (Recursive sub-profile) and those who
did not (Non-recursive sub-profile). Table 3 displays the formulae for calculating the
four different profiles.
Drafter
(P2 + P3) ÷ P1 < 1/6
Reviser
P1 ÷ (P2 + P3) < 1/6
Drafter Non-Recursive Reviser
(P2 + P3) ÷ P1
1/6 & P2 ÷ P3 < 1/6
Drafter Recursive Reviser
(P2 + P3) ÷ P1
1/6 & P2 ÷ P3 1/6
Table 3: calculation of translator profiles per types of macro TUs where < or >1/6 is a distinctive indicator
116
4.2
On drafting and revision in translation
Patterns of translator profiles in the drafting and in the revision phases
According to our analysis, we identified four types of profiles, namely Drafters,
Revisers, Drafter Non- Recursive Revisers, and Drafter Recursive Revisers. Drafters
are those subjects who predominantly show P1 types of macro translation units and
process them entirely during the drafting phase. Revisers, on the other hand, seem to
produce interim solutions in the provisional target text while drafting and
implementing changes predominantly in the revision phase. As far as the third and
fourth profiles are concerned, those of the Drafter/Reviser, all subjects had
approximately the same number of TT changes in both phases, which can be
expressed by ½ < P1 ÷ (P2 + P3) < 2.
The data analysis shows that neither 1/6 < (P2 + P3) ÷ P1 < 1/2 nor 1/6 < P1 ÷ (P2 +
P3) < ½ were observed in the sample. In other words, either the subject had an
approximate equal number of changes during the drafting and revision phases or the
subject implemented a lot more changes in one phase than in the other. In our
corpus, there is no subject with a tendency to revise slightly more in one of the two
phases. There are two trends in the sample: a predominant mode of revision either
during the drafting or revision phases or a strong tendency towards a balanced
distribution of P1 and P2 types of macro translation units.
When determining the “Drafter Recursive Reviser” profile, all translators of the
Drafter Reviser profile were found to have approximately six times more changes
implemented of type P2 than those of type P3. The ones that are over the threshold of
6 P2s per P3 are on the “Drafter Non-Recursive Reviser” profile and the ones who
were below this threshold were on the “Drafter Recursive Reviser” profile. Again, all
translators were close to this threshold. Therefore, these two categories can be
understood as slight tendencies in a cline.
At last, by definition, there must be at least one change during the drafting phase
for identifying a textual change of the type P3, which can be expressed as P1 > 0 if P3
> 0. Although this is the only rule that must be found by definition, we also found
two other rules: in every analysed translation, there were more changes in the
drafting phase (P1) than recursive changes in the revision phase (P3) and there were
always more non-recursive changes in the revision phase (P2) than recursive ones
(P3), what can be expressed as P1 > P3 and P2 > P3.
4.3
Patterns of macro translation units in the drafting and in the revision phases
Besides classifying the data in terms of macro translation units of types P1, P2 and P3
as well as introducing four different translator profiles, the data analysis also allows
the observation of subpatterns within the triadic categories. By looking at the data,
one observes how decisions previously made by the translator influence the revision
patterns in the unfolding of the macro translation units. On the one hand, translation
process data such as key-logging is linear in time – one event at a time follows
another – and recursive in the TT: additions, editions and deletions may happen in
any position of it. On the other hand, TTs have a linear structure: their characters – in
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all their intermediate and final versions – are organized linearly – one character after
the other. When translating a given micro unit, a choice made at timestamp X may
lead the translator to replace a decision made in a previous part of the TT at
timestamp Y by an alternative which signals an attempt to standardize choices. This
upward movement has been classified as a P1 ascending pattern as shown in Figure
4.
Figure 4 - P1 ascending pattern (example of G4 performance)
As displayed in the upper part of Figure 4, one can see that, as shown at
timestamp 471290ms, G4 initially translates the German verb “bestimmen”
[determine] into Brazilian Portuguese as “determinar” [determinar]. As the process
unfolds, two lexical items are translated as “medição” and “medida” [measurement].
Then, as shown at timestamp 557820ms, still in the drafting phase, after translating
the noun “Bestimmung” [determination] as “medição” [measurement], G4 changes
“determinar” [determine] into “mensurar” [measure]. This upward recursive
movement in text production seems to be clearly driven by the lexical choices of
“medição”/“medida” [measurement] and “medição” [measurement] which lead G4 to
replace “determinar” by “mensurar”. The upward unfolding of the micro units into a
macro unit in the drafting phase illustrates what we call a P1 ascending pattern.
When translating another given micro unit, a first choice may be replaced by a
second alternative which indicates that a previously made decision influences the
revision carried out by the translator in an attempt to standardize choices. This
downward movement has been classified as a P1 descending pattern as shown in
Figure 5.
118
On drafting and revision in translation
Figure 5 - P1 descending pattern (example of G6 performance)
As displayed in the upper part of Figure 5, one can see that, at timestamp 690820ms,
while translating the same source text fragment, G6 initially translates the German
verb “bestimmen” [determine] into Brazilian Portuguese as “verificar” [verify]. Figure
5 also shows that “Bestimmungen” [determinations] down below in the same source
text fragment was translated as “averiguações” [investigations]. As the process
unfolds, at timestamp 738950ms, still in the drafting phase, G6 changes “averiguações”
[investigations] into “verificações” [verifications]. This downward recursive
movement in text production seems to be clearly driven by the lexical choice of
“verificar” [verify] at shown timestamp 690820ms. The downward unfolding of the
micro units into a macro unit in the drafting phase illustrates what we call a P1
descending pattern.
Both ascending and descending subtypes of P1 signal the influence of different
stages of text production in the unfolding of macro translation units. What must be
clear is that the notion of descending and ascending movements is related to but is
not the same as the one of previous and following positions in the TT. The former are
dynamic movements of the subjects over the TT in a process-oriented perspective
and the latter are static relative positions of text segments in a product-oriented
perspective. Sometimes the driving force is a translation decision made later in the
drafting phase which influences the revision of a choice which had already been
made earlier in the translation process (P1 ascending pattern). At other times, the
driving force is a previously made decision which seems to guide the revision of a
translation alternative which is then implemented on the basis of a choice made at a
previous timestamp (P1 descending pattern).
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119
Additionally, similar processes of descending types of macro units seem to occur
when we move away from the drafting phase. Given our observations of P-types, P2
only shows a descending pattern. In this subtype of macro translation unit, a micro
unit occurs only once in the drafting phase and is then processed once or more in the
revision phase.
Figure 6 – P2 descending pattern (example of E3 performance)
Figure 6 displays an example of a P2 descending pattern. As displayed in the
upper part of Figure 6, one can see that E3 initially translates the pair “adjust” and
“set up” by “regular” [regulate] and “definiu” [defined]. E3 then changes “definiu”
[defined] into “regulou” [regulated] during the revision phase. The downward
unfolding of the micro units into a macro unit in the revision phase illustrates what
we call a P2 descending pattern.
Finally, as shown in Figure 7, a descending pattern also seems to be prototypical of
P3. One can see that G6 translates the word set “bestimmen”, “Messbereich”, “Bereich”,
“kontrollieren”, “Bestimmungen” by “verificar” [verify], “âmbito de aferição” [scope of
verification], “âmbito de aferição” [scope of verification], “verifique” [verify],
“verificações” [verifications] and then changes “verificações” [verifications] into
“aferições” [verifications] in the revision phase. These examples of changes in the
revision phase show a revision process that is not bound to the lexical
correspondences between the source and target languages/texts.
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On drafting and revision in translation
Figure 7 – P3 descending pattern (example of G6 performance)
5
Concluding remarks
The picture emerging from the data analysis is manifold. Using the LITTERAE
annotation and search tool, it was possible to classify macro translation units
according to types P1, P2 and P3. It was also possible to differentiate two main types
of macro translation units. On the one hand, P1 can be considered as a type of macro
unit which signals online cognitive processing of translation units both in ascending
and descending modes. On the other hand, P2 and P3 can be seen as types of macro
units which signal a somewhat different process, namely a process that is more
detached from the source text and consists of revisions of text production rather than
translations per se. This difference is quite striking particularly in view of the fact
that both P2 and P3 are descending modes of text production in translation. On the
whole, P2 types are more frequent that P3 types and more substantial revisions are
only found among P2 types of macro translation units. P3 types seem to account for
more fine-grained revisions which are quite small in numbers.
The overall trend shows that in terms of cognitive processing P1 has quite a
distinctive nature than that of P2 and P3 and seems to be where translation takes
place par excellence. However, the amount of data analysed in this paper is too small
to allow generalizations. Nevertheless, we hope to have paved the way for future
studies by presenting a tool and a methodology which can be replicated and, thus,
foster a corpus linguistics oriented analysis of translation process data.
6
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